
"""simple docstring"""



def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Optional[int]:





    """simple docstring"""

    return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2


def 			_UpperCamelCase						(    _A       ,   _A=0   )     ->	Union[str, Any]:





    """simple docstring"""

    return sorted(_A       ,   key=lambda _A   : x[column]   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=float("""inf"""   )   )     ->	List[str]:





    """simple docstring"""

    for i in range(points_counts - 1   ):
        for j in range(i + 1       ,   _A   ):
            _UpperCAmelCase        = euclidean_distance_sqr(points[i]       ,   points[j]   )
            if current_dis < min_dis:
                _UpperCAmelCase        = current_dis
    return min_dis


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=float("""inf"""   )   )     ->	Optional[Any]:





    """simple docstring"""

    for i in range(min(6       ,   points_counts - 1   )       ,   _A   ):
        for j in range(max(0       ,   i - 6   )       ,   _A   ):
            _UpperCAmelCase        = euclidean_distance_sqr(points[i]       ,   points[j]   )
            if current_dis < min_dis:
                _UpperCAmelCase        = current_dis
    return min_dis


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    if points_counts <= 3:
        return dis_between_closest_pair(_A       ,   _A   )

    # recursion
    _UpperCAmelCase        = points_counts // 2
    _UpperCAmelCase        = closest_pair_of_points_sqr(
        _A       ,   points_sorted_on_y[:mid]       ,   _A   )
    _UpperCAmelCase        = closest_pair_of_points_sqr(
        _A       ,   points_sorted_on_y[mid:]       ,   points_counts - mid   )
    _UpperCAmelCase        = min(_A       ,   _A   )
    _UpperCAmelCase        = []
    for point in points_sorted_on_x:
        if abs(point[0] - points_sorted_on_x[mid][0]   ) < closest_pair_dis:
            cross_strip.append(_A   )

    _UpperCAmelCase        = dis_between_closest_in_strip(
        _A       ,   len(_A   )       ,   _A   )
    return min(_A       ,   _A   )


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = column_based_sort(_A       ,   column=0   )
    _UpperCAmelCase        = column_based_sort(_A       ,   column=1   )
    return (
        closest_pair_of_points_sqr(
            _A       ,   _A       ,   _A   )
    ) ** 0.5


if __name__ == "__main__":
 a       :						Optional[Any]       		=		[(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
 print('''Distance:''', closest_pair_of_points(points, len(points)))

"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a       :						List[Any]       		=		logging.get_logger(__name__)

a       :						str       		=		{
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}




class 		a_    (			_UpperCAmelCase						):
    a   :					Any			     =					'camembert'







    def __init__(      self      :							Dict  ,       __UpperCamelCase      :							Union[str, Any]=3_05_22  ,       __UpperCamelCase      :							Tuple=7_68  ,       __UpperCamelCase      :							Optional[Any]=12  ,       __UpperCamelCase      :							int=12  ,       __UpperCamelCase      :							int=30_72  ,       __UpperCamelCase      :							int="gelu"  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							List[Any]=5_12  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Dict=0.0_2  ,       __UpperCamelCase      :							Optional[int]=1e-12  ,       __UpperCamelCase      :							Optional[Any]=1  ,       __UpperCamelCase      :							int=0  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Optional[int]="absolute"  ,       __UpperCamelCase      :							int=True  ,       __UpperCamelCase      :							Dict=None  ,       **__UpperCamelCase      :							List[str]  ,       )		->Union[str, Any]:






        '''simple docstring'''






        super().__init__(pad_token_id=__UpperCamelCase  ,       bos_token_id=__UpperCamelCase  ,       eos_token_id=__UpperCamelCase  ,       **__UpperCamelCase  )

        _UpperCAmelCase        = vocab_size
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = max_position_embeddings
        _UpperCAmelCase        = type_vocab_size
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = layer_norm_eps
        _UpperCAmelCase        = position_embedding_type
        _UpperCAmelCase        = use_cache
        _UpperCAmelCase        = classifier_dropout







class 		a_    (			_UpperCAmelCase						):







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->Mapping[str, Mapping[int, str]]:






        '''simple docstring'''






        if self.task == "multiple-choice":
            _UpperCAmelCase        = {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _UpperCAmelCase        = {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]  )